This invention relates generally to magnetic resonance imaging (MRI), and more particularly the invention relates to MRI with parallel imaging.
Magnetic resonance imaging (MRI) is a non-destructive method for the analysis of materials and is an approach to medical imaging. It is generally non-invasive and does not involve ionizing radiation. In very general terms, nuclear magnetic moments are excited at specific spin precession frequencies which are proportional to the local magnetic field. The radio-frequency signals resulting from the precession of these spins are received using pickup coils. By manipulating the magnetic fields, an array of signals is provided representing different regions of the volume. These are combined to produce a volumetric image of the nuclear spin density of the body.
Magnetic resonance (MR) imaging is based on nuclear spins, which can be viewed as vectors in a three-dimensional space. During an MRI experiment, each nuclear spin responds to four different effects: precession about the main magnetic field, nutation about an axis perpendicular to the main field, and both transverse and longitudinal relaxation. In steady-state MRI experiments, a combination of these effects occurs periodically.
Parallel imaging uses multiple receiver coils that each receives signals from a subset of the total volume and combines data of the multiple receiver coils to provide an image for a total volume.
Parallel imaging methods exploit the sensitivity of the receiver coils to accelerate MRI acquisitions. SENSE based reconstructions, as described in Pruessmann et al., “Advances in sensitivity encoding with arbitrary k-space trajectories,” MRM 46(4):638-51 (2001), provide a complete general reconstruction from arbitrary k-space sampling. SENSE attempts to reconstruct the imaged object exactly, with no coil weighting. To do so, it requires an accurate explicit measurement of the coils sensitivity. The GRAPPA based reconstructions, as described in Griswold et al, “Generalized autocalibrating partially parallel acquisitions (GRAPPA),” MRM 47(6):1202-10 (2002), are becoming increasingly more popular. GRAPPA type reconstructions do not attempt to reconstruct the exact original object. Instead, they attempt to reconstruct each coil image separately—a significantly relaxed requirement. Therefore it requires only implicit coils sensitivity information in the form of correlations between pixels in k-space. The correlation measurements are obtained by calibration. However GRAPPA reconstruction is not as general as SENSE. Although several non-Cartesian solutions for GRAPPA reconstructions have been proposed, the proposed methods are either approximate, or tailored for specific sampling trajectories.
FIG. 9a is a schematic illustration of a conventional GRAPPA process. In this example a linear k-space scan is used to acquire reduced data 908 for every other row in a Cartesian grid 904. Other Cartesian grids 906 are shown to illustrate reduced data acquired for other coils used in the parallel process. In a calibration region 912 calibration data 916 is fully acquired. A GRAPPA synthesizing filter is generated from the calibration data 916. FIG. 9b schematically shows the generation of a conventional GRAPPA synthesizing filter. In this example, data points 920 above and below a center data point 924 are used to generate the conventional GRAPPA synthesizing filter. Data points 928 on the side of the center data point 924 are not used to generate the conventional GRAPPA synthesizing filter. This is because the scanning pattern is known and the scanning pattern does not scan data points on the sides of the data point being determined by the filter and the conventional GRAPPA uses this known scanning pattern to provide a synthesizing filter.
FIG. 9c is a schematic illustration of the use of the conventional GRAPPA synthesizing filter. A synthesizing filter 932 uses data points above and below a center point 936 to synthesize data for the center point 936. The synthesizing filter may be applied along each row of the Cartesian grid 904 where k-space data was not acquired to synthesize data 940 for each point in the row.